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Weakly-supervised semantic segmentation (WSSS) with image-level labels hasbeen widely studied to relieve the annotation burden of the traditionalsegmentation task. In this paper, we show that existing fully-annotated basecategories can help segment objects of novel categories with only image-levellabels, even if base categories and novel categories have no overlap. We referto this task as weak-shot semantic segmentation, which could also be treated asWSSS with auxiliary fully-annotated categories. Recent advanced WSSS methodsusually obtain class activation maps (CAMs) and refine them by affinitypropagation. Based on the observation that semantic affinity and boundary areclass-agnostic, we propose a method under the WSSS framework to transfersemantic affinity and boundary from base to novel categories. As a result, wefind that pixel-level annotation of base categories can facilitate affinitylearning and propagation, leading to higher-quality CAMs of novel categories.Extensive experiments on PASCAL VOC 2012 dataset prove that our methodsignificantly outperforms WSSS baselines on novel categories.

Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary